Efficient GPU Retrieval for Semantic Search
Semantic Search on LinkedIn must retrieve relevant profiles from a corpus of hundreds of millions in response to natural-language queries such as "a fintech founder in Berlin who worked in payments." The deployed relevance policy is bottleneck-oriented: every active non-negotiable facet must be satisfied, and a pre-existing LLM Graded Relevance (GR) judge operationalizes this through a fixed min/median aggregation over facet grades. Cosine similarity instead averages evidence, letting a strong match on one facet mask failure on another, capping the recall of the first-stage (L0) retriever. We present a policy-aligned retrieval framework: embeddings are partitioned into eight category-supe...
| Authors | Dhritiman Das, Chujie Zheng, Ronak Kaoshik et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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